图 书 馆
关于mkl的高斯过程观点A Gaussian Process View on MKL |
|
| 课程网址: | http://videolectures.net/nipsworkshops2010_urtasun_gpv/ |
| 主讲教师: | Raquel Urtasun |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Gaussian processes (GPs) provide an appealing probabilistic framework for multiple kernel learning (MKL). For more than a decade, it has been common practice to learn the well known sum-of-kernels by, for example, maximum likelihood estimation. In this talk, I’ll first introduce the sum-of-kernels Gaussian process formulation. I’ll then show how to go beyond convex formulations by learning the GP covariance. In particular, I’ll first introduce parametric forms of the covariance driving connections to co- training. I’ll then show how to learn non-parametric covariances via latent spaces. If time permits, I’ll talk about multi-task learning as well as multi-output Gaussian processes and show connections to metric learning. I’ll demonstrate the performance of some of these approaches in computer vision tasks such as object recognition. |
| 关 键 词: | 高斯过程; 多核学习; 概率框架 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 58 |